Object detection has achieved significant progress in attaining high-quality performance
without leaking private messages. However, traditional approaches cannot defend the …

Auditing of AI systems is a promising way to understand and manage ethical problems and
societal risks associated with contemporary AI systems, as well as some anticipated future …

Owing to their remarkable learning capabilities and performance in real-world applications,
the use of machine learning systems based on Neural Networks (NNs) has been …

Modern day smart cyber-physical systems (CPS) and Internet of Things (IoTs), including
those deployed in critical devices such as wearables, often use embedded machine …

Machine learning (ML) has emerged as the principal tool for performing complex tasks
which are impractical (if not impossible) to code by humans. ML techniques provide …

Adversarial attacks are deliberate data manipulations that may appear harmless to the
viewer yet lead to incorrect categorization in a machine learning or deep learning system …

Abstract Machine learning methods have become a commodity in the toolkits of both
researchers and practitioners. For performance and privacy reasons, new applications often …

This thesis proposes a security by design framework for identifying and mitigating cyber and
physical threats on CAVs. A structured security engineering process for threat identification …

ABSTRACT Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep
Learning (DL) is already present in many applications ranging from computer vision for …

Deep Neural Networks (DNN) are very effective in high performance applications such as
computer vision, natural language processing and speech recognition. However, these …